Not accounting for the hidden covariates, Z, can reduce power and
result in poor control of false discovery rate. The
vicar package provides a suite
of functions to adjust for hidden confounders, both when one has and
does not have access to control genes.

The functions mouthwash and backwash can adjust for hidden
confounding when one does not have access to control genes. They do so
via non-parametric empirical Bayes methods that use the powerful
methodology of Adaptive SHrinkage (Stephens 2016) within the
factor-augmented regression framework described in Wang et
al. (2015). backwash is a slightly more Bayesian version of
mouthwash.

When one has control genes, there are many approaches to take. Such
methods include RUV2 (J. A. Gagnon-Bartsch and Speed 2012), RUV4
(J. Gagnon-Bartsch, Jacob, and Speed 2013), and CATE (Wang et
al. 2015). This package adds to the field of confounder adjustment
with control genes by

(1) Implementing a version of CATE that is calibrated using control genes similarly to the method in J. Gagnon-Bartsch, Jacob, and Speed (2013). The function is called vruv4.

(2) Introduces RUV3, a version of RUV that can be considered both RUV2 and RUV4. The function is called ruv3.

(3) Introduces RUV-impute, a more general framework for accounting for hidden confounders in regression. The function is called ruvimpute

(4) Introduces RUV-Bayes, a Bayesian version of RUV. The function is called ruvb.